Brainstorm Pipeline Analysis of Resting-State Data From the Open MEG Archive

Niso, Guiomar; Tadel, François; Bock, Elizabeth; Cousineau, Martin; Santos, A.; Baillet, Sylvain

doi:10.3389/fnins.2019.00284

Cited by 66 publications

(73 citation statements)

References 29 publications

(38 reference statements)

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“…Each MEG dataset was individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension (tSSS; MaxFilter v2.2; correlation limit: 0.950; correlation window duration: 6 seconds; Taulu and Simola, 2006 ). MEG data processing then largely followed the same analysis pipeline outlined in ( Niso et al, 2019 ). Noise-reduced MEG data underwent standard data preprocessing procedures using the Brainstorm software ( Tadel et al, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

Three-year reliability of MEG resting-state oscillatory power

et al. 2021

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Introduction: Resting-state oscillatory activity has been extensively studied across a wide array of disorders. Establishing which spectrally- and spatially-specific oscillatory components exhibit test-retest reliability is essential to move the field forward. While studies have shown short-term reliability of MEG resting-state activity, no studies have examined test-retest reliability across an extended period of time to establish the stability of these signals which is critical for reproducibility. Methods: We examined 18 healthy adults age 23 – 61 who completed three visits across three years. For each visit participants completed both a resting state MEG and structural MRI scan. MEG data were source imaged, and the cortical power in canonical frequency bands (delta, theta, alpha, beta, low gamma, high gamma) was computed Intra-class correlation coefficients (ICC) were then calculated across the cortex for each frequency band. Results: Over three years, power in the alpha and beta bands displayed the highest reliability estimates, while gamma showed the lowest estimates of three-year reliability. Spatially, delta, alpha, and beta all showed the highest degrees of reliability in the parietal cortex. Interestingly, the peak signal for each of these frequency bands was located outside of the parietal cortex, suggesting that reliability estimates were not solely dependent on the signal-to-noise ratio. Conclusion: Oscillatory resting-state power in parietal delta, posterior beta, and alpha across most of the cortex are reliable across three years and future MEEG studies may focus on these measures for the development of specific markers.

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Section: Methodsmentioning

confidence: 99%

Three-year reliability of MEG resting-state oscillatory power

et al. 2021

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“…It enforces GSP with powerful data exploration and interactive visualization tools, and a database engine that handles data files transparently for the user. It is therefore particularly well suited to adopt the current move towards standardized data handling (see BIDS section) (Niso et al, 2019). Importantly, Brainstorm can handle multiple data types in addition to MEG, scalp and intracranial EEG within the same protocol.…”

Section: Box 2: a Brief Historical Perspective On Gsp Of Meeg Analysis Softwarementioning

confidence: 99%

Good Scientific Practice in MEEG Research: Progress and Perspectives

Niso¹,

Krol²,

Combrisson³

et al. 2021

Preprint

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Good Scientific Practice (GSP) refers to both explicit and implicit rules or guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated whenever new findings come to light. However, GSP also needs to be periodically revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasised intangible GSP: a general awareness of personal, organisational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, GSP with respect to data acquisition, analysis, reporting, and sharing is discussed, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favour collective and cooperative work, for both scientific and for societal reasons.

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“…In practice, to facilitate the source connectivity analysis of MEG signals, several open-source applications are available to the user (Table 1), for example, Brainstorm (Tadel et al, 2011;Niso et al, 2015Niso et al, , 2019, eConnectome (He et al, 2011), FieldTrip (Oostenveld et al, 2011), MNE (Gramfort et al, 2014), and SPM (Litvak et al, 2011), which provide a platform for the standardization of the most common analysis processes and reduce sharing efforts across MEG communities.…”

Section: Resting-state Functional Connectivity Based On Meg Signalsmentioning

confidence: 99%

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

Shan

et al. 2021

Front. Hum. Neurosci.

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Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source–space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.

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Brainstorm Pipeline Analysis of Resting-State Data From the Open MEG Archive

Cited by 66 publications

References 29 publications

Three-year reliability of MEG resting-state oscillatory power

Three-year reliability of MEG resting-state oscillatory power

Good Scientific Practice in MEEG Research: Progress and Perspectives

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

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